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https://issues.apache.org/jira/browse/ARROW-15716?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17634264#comment-17634264
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Joris Van den Bossche commented on ARROW-15716:
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To just OR-combine the different expressions for each of the paths, you can do
this automatically with {{reduce()}} and a list comprehension calling
Partitioning.parse on each of the paths (without having to resort to
{{_get_partition_keys}} and {{filters_to_expression}}). Using your example:
{code}
paths = ['path/to/data/month_id=202105/v1-manual__2022-11-06T22:50:20.parquet',
'path/to/data/month_id=202106/v1-manual__2022-11-06T22:50:20.parquet',
'path/to/data/month_id=202107/v1-manual__2022-11-06T22:50:20..parquet']
partitioning = ds.partitioning(pa.schema([('month_id', 'int64')]),
flavor="hive")
>>> import operator
>>> import functools
>>> functools.reduce(operator.or_, [partitioning.parse(file) for file in paths])
<pyarrow.compute.Expression (((month_id == 202105) or (month_id == 202106)) or
(month_id == 202107))>
{code}
I think this is what Weston is suggesting to do. It doesn't necessarily give
the most efficient filter expression, but that's a direct translation of the
subset of paths (if there are many paths, it might be more efficient with isin
or a greater/smaller compare kernel)
> [Dataset][Python] Parse a list of fragment paths to gather filters
> ------------------------------------------------------------------
>
> Key: ARROW-15716
> URL: https://issues.apache.org/jira/browse/ARROW-15716
> Project: Apache Arrow
> Issue Type: Wish
> Components: Python
> Affects Versions: 7.0.0
> Reporter: Lance Dacey
> Assignee: Vibhatha Lakmal Abeykoon
> Priority: Minor
> Labels: pull-request-available
> Time Spent: 0.5h
> Remaining Estimate: 0h
>
> Is it possible for partitioning.parse() to be updated to parse a list of
> paths instead of just a single path?
> I am passing the .paths from file_visitor to downstream tasks to process data
> which was recently saved, but I can run into problems with this if I
> overwrite data with delete_matching in order to consolidate small files since
> the paths won't exist.
> Here is the output of my current approach to use filters instead of reading
> the paths directly:
> {code:python}
> # Fragments saved during write_dataset
> ['dev/dataset/fragments/date_id=20210813/data-0.parquet',
> 'dev/dataset/fragments/date_id=20210114/data-2.parquet',
> 'dev/dataset/fragments/date_id=20210114/data-1.parquet',
> 'dev/dataset/fragments/date_id=20210114/data-0.parquet']
> # Run partitioning.parse() on each fragment
> [<pyarrow.compute.Expression (date_id == 20210813)>,
> <pyarrow.compute.Expression (date_id == 20210114)>,
> <pyarrow.compute.Expression (date_id == 20210114)>,
> <pyarrow.compute.Expression (date_id == 20210114)>]
> # Format those expressions into a list of tuples
> [('date_id', 'in', [20210114, 20210813])]
> # Convert to an expression which is used as a filter in .to_table()
> is_in(date_id, {value_set=int64:[
> 20210114,
> 20210813
> ], skip_nulls=false})
> {code}
> My hope would be to do something like filt_exp = partitioning.parse(paths)
> which would return a dataset expression.
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